Abstract
The aim of the study was to describe student profiles associated with educational achievement from four sets of personal variables: psychological, academic, health, and technology use. A representative sample of middle school students from Baja California, Mexico, was given an ad hoc questionnaire and academic results were obtained from a standardized academic achievement test. Cluster analysis k-means allowed us to define two student profiles. Equal numbers of students were found in both groups, with Cluster 1 grouping high-achieving students and Cluster 2 those with a lower level of educational achievement. All of the personal variables help to describe and differentiate between the two groups, except for the number of times they have changed schools.
Highlights
Such analytical procedures contribute to configuring various types of student profiles, entailing an understanding of the variables related to educational achievement, this type of technique has not been used frequently and the variables involved tend to be linked to, and restricted to, specific fields, which suggests the use of more comprehensive analysis schemes (De Groot et al, 2015)
Two perfectly-defined student profiles associated with academic achievement were identified through the results obtained: Cluster 1 brought together students who exhibited higher educational achievement, and Cluster 2, students with lower educational achievement
It should be stressed that this study included a greater and more diverse set of variables than other studies (Chaparro et al, 2016; Corpus & Wormington, 2014; De Groot et al, 2015; Dull et al, 2015; Ning & Downing, 2015; Pellicer et al, 2015; Rodríguez-Ayán, 2010; Rogers, 2013; Sparks et al, 2012; Valle et al, 2008; Valle et al, 2009), and all these variables contributed to describing the different student profiles
Summary
An interest in understanding the variables that explain students’ attainment has furthered the development of various methodological proposals and analysis techniques, and in particular those that focus on obtaining and characterizing student profiles and the variables associated with their levels of achievement (Bravo, Salvo & Muñoz, 2015; Chaparro, González & Caso, 2016; Corpus & Wormington, 2014; Da Silva, Nunes, Santos, Queiroz, & Leles, 2010; De Groot, van Dijk, & Kirschner, 2015; Dull, Schleifer, & McMillan, 2015; Hayenga & Corpus, 2010; Linnakylä & Malin, 2008; Ning & Downing, 2015; Pellicer, García, Morales, Serra, Solana, González, & Toca, 2015; Rodríguez-Ayán, 2010; Sparks, Patton, & Ganschow, 2012; Valle et al, 2008; Valle et al, 2009). such analytical procedures contribute to configuring various types of student profiles, entailing an understanding of the variables related to educational achievement, this type of technique has not been used frequently and the variables involved tend to be linked to, and restricted to, specific fields, which suggests the use of more comprehensive analysis schemes (De Groot et al, 2015). An interest in understanding the variables that explain students’ attainment has furthered the development of various methodological proposals and analysis techniques, and in particular those that focus on obtaining and characterizing student profiles and the variables associated with their levels of achievement (Bravo, Salvo & Muñoz, 2015; Chaparro, González & Caso, 2016; Corpus & Wormington, 2014; Da Silva, Nunes, Santos, Queiroz, & Leles, 2010; De Groot, van Dijk, & Kirschner, 2015; Dull, Schleifer, & McMillan, 2015; Hayenga & Corpus, 2010; Linnakylä & Malin, 2008; Ning & Downing, 2015; Pellicer, García, Morales, Serra, Solana, González, & Toca, 2015; Rodríguez-Ayán, 2010; Sparks, Patton, & Ganschow, 2012; Valle et al, 2008; Valle et al, 2009). Existing studies that focus on student profile characterization tend to include personal variables, either of a psychological or affective-motivational nature.
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